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cal_size_density.py
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cal_size_density.py
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''''
Use Gaussian fitting to correct the position,
and calculate the intensity and diameter of particles.
Output new Dataframe
'''
import numpy as np
import os
import argparse
import glob
import pandas as pd
import cv2
import numpy as np
# from astrops.io import fits as pf
import matplotlib.pyplot as plt
import os
import sys
# from astropy.wcs import WCS
import pandas as pd
# import matplotlib.patches as patches
from scipy import optimize
def parse_args_():
parser = argparse.ArgumentParser(description='Train keypoints network')
parser.add_argument('--det_folder', type=str, default='./Log/20240327_09_54_10_lamp_vesicle4_eval/prediction_0.99')
# param
# parser.add_argument('--diameter_filter', type=float, default=0.0)
# parser.add_argument('--density_filter', type=float, default=0.0)
parser.add_argument('--gaussfit_search_radius', type=int, default=4)
parser.add_argument('--img_folder', type=str, default='/mnt/data1/ZYDdata/lwj/200609 lamp-1mch int2s 013 ori')
parser.add_argument('--save_folder',type=str, default='./Log/20240327_09_54_10_lamp_vesicle4_eval/prediction_0.99_cal1')
# parser.add_argument('--gaussfit_diameter')
args = parser.parse_args()
return args
def gaussian(height, center_x, center_y, width_x, width_y):
"""Returns a gaussian function with the given parameters"""
width_x = float(width_x)
width_y = float(width_y)
return lambda x,y: height*np.exp(
-(((center_x-x)/width_x)**2+((center_y-y)/width_y)**2)/2)
def moments(data):
"""Returns (height, x, y, width_x, width_y)
the gaussian parameters of a 2D distribution by calculating its
moments """
total = data.sum()
X, Y = np.indices(data.shape)
x = (X*data).sum()/total
y = (Y*data).sum()/total
col = data[:, int(y)]
width_x = np.sqrt(np.abs((np.arange(col.size)-x)**2*col).sum()/col.sum())
row = data[int(x), :]
width_y = np.sqrt(np.abs((np.arange(row.size)-y)**2*row).sum()/row.sum())
height = data.max()
return height, x, y, width_x, width_y
def fitgaussian(data):
"""Returns (height, x, y, width_x, width_y)
the gaussian parameters of a 2D distribution found by a fit"""
params = moments(data)
errorfunction = lambda p: np.ravel(gaussian(*p)(*np.indices(data.shape)) -
data)
p, success = optimize.leastsq(errorfunction, params)
return p
if __name__ == "__main__":
opt = parse_args_()
r_S = opt.gaussfit_search_radius
savefo = opt.save_folder
os.makedirs(savefo, exist_ok=True)
# diameter_thre = opt.diameter_filter
# density_thre = opt.density_filter
# save path
# savepath = opt.det_folder+'_filter_di{:.1f}_de{:.1f}'.format(opt.diameter_filter, opt.density_filter)
allcsvresultpathlist = glob.glob(os.path.join(opt.det_folder, '**.csv'))
allcsvresultpathlist.sort()
# alldiameter = []
# alldensity = []
for csvpa in allcsvresultpathlist:
# add new col
df = pd.read_csv(csvpa, header=0, index_col=None)
df['diameter'] = 0.0
df['density'] = 0.0
# read the frame
csvname = os.path.split(csvpa)[-1]
imgname = csvname.replace('.csv','.tif')
imgpa = os.path.join(opt.img_folder,imgname)
# imgpa = csvpa[:-3]+'png'
theimg = cv2.imread(imgpa)
theimg = cv2.cvtColor(theimg, cv2.COLOR_BGR2GRAY)
theimg = cv2.cvtColor(theimg, cv2.COLOR_GRAY2BGR)
theimg = theimg[:,:,0] #yellow circle in the last two channels
imgheight, imgwidth = theimg.shape
# print(imgheight,imgwidth)
# iterate each point
for i in df.index:
left = df.iloc[i].loc['pos_x']
top = df.iloc[i].loc['pos_y']
# print(left,top,r_S)
l = max(int(left)-r_S, 0)
r = min(int(left)+r_S+1, imgwidth - 1)
t = max(int(top)-r_S, 0)
b = min(int(top)+r_S+1, imgheight - 1)
# print(int(top)+r_S+1)
# print(t,b,l,r)
# Create the gaussian data
# Xin, Yin = np.mgrid[0:201, 0:201]
# data = gaussian(3, 100, 100, 20, 40)(Xin, Yin) + np.random.random(Xin.shape)
data = theimg[t:b, l:r]
params = fitgaussian(data)
(height, x, y, width_x, width_y) = params # x==top' y==left'
x = t+x
y = l+y
# 判断是否有gauss点，如果拟合的坐标在外面，先绘制一下看看有哪些情况
if x<t or y<l or x>b or y>r or width_x is None or width_y is None or np.isnan(width_x) or np.isnan(width_y):
if np.isnan(width_x) or np.isnan(width_y): print(f'{i} width is nan')
# plt.matshow(data)#, cmap=plt.cm.gist_earth_r)
# fit = gaussian(*params)
# plt.contour(fit(*np.indices(data.shape)), cmap=plt.cm.copper)
# ax = plt.gca()
# plt.text(0.95, 0.05, """
# x : %.1f
# y : %.1f
# orix: %.1f
# oriy: %.1f
# width_x : %.1f
# width_y : %.1f""" %(x, y, top,left,width_x, width_y),
# fontsize=16, horizontalalignment='right',
# verticalalignment='bottom', transform=ax.transAxes)
# plt.savefig(os.path.join(savefo,f'{csvname[:-4]}-{i}.png'))
# plt.close()
df.at[i, 'diameter'] = -1.0
df.at[i, 'density'] = theimg[int(top),int(left)]*2
else:
# save new csv
df.at[i, 'pos_x'] = y
df.at[i, 'pos_y'] = x
df.at[i, 'diameter'] = min(width_x, width_y)*2
df.at[i, 'density'] = theimg[int(x),int(y)]*2
# save filter results
df.to_csv(os.path.join(savefo, csvname), index=False)